Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.

本研究提出了一种基于量子电路学习的算法，可以利用本地门和量子位连接等量子硬件能力来协助量子设备的表征，并训练浅层电路进行生成式任务。此方法可以学习绿伯格 - 霍恩 - 泽林格（Greenberger-Horne-Zeilinger）状态的最佳制备方法，也可以有效地制备连续热态的近似表示，其波函数在其幅度中编码玻尔兹曼概率。最后，研究提供了一种新的硬件无关指标，称为 qBAS 得分，可用于衡量近期量子设备的性能。